1. Field of the Invention
This invention relates to filtering in general and to filtering ultrasound data in particular.
2. Prior Art
Ultrasonic imaging is a frequently used method of analysis for examining a wide range of media and objects. Ultrasonic imaging is especially common in medicine because of its relatively non-invasive nature, low cost, and fast response times. For example, ultrasonic imaging is commonly used to detect and monitor the growth and health of fetuses, or to detect and assist in the diagnosis of liver and kidney pathology. Typically, ultrasonic imaging is accomplished by generating and directing ultrasonic sound waves (an ultrasonic beam or signal) into a medium under investigation using a set of ultrasound generating transducers and then observing reflections generated at the boundaries of dissimilar materials, such as tissues within a patient, also using a set of ultrasound receiving transducers. The generating and receiving transducers may be arranged in arrays and a single transducer may be used for both generating and receiving ultrasonic signals. The reflections are converted to electrical signals by the receiving transducers and then processed, using techniques known in the art, to determine the locations of echo sources. The resulting data is displayed using a display device, such as a monitor.
Typically, the ultrasonic signal transmitted into the medium under investigation is generated by applying continuous or pulsed electronic signals to an ultrasound generating transducer. In diagnostic imaging, the transmitted ultrasonic signal is generally in the radio frequency A) range of 1 MHz to 15 MHz, which corresponds to ultrasonic wavelengths in the range of 0.1 mm to 1.5 mm. The ultrasonic signal propagates through the medium under investigation and reflects off interfaces, such as boundaries, between adjacent tissue layers. Scattering of the ultrasonic signal refers to the deflection of the ultrasonic signal in many directions by interfaces that are much smaller than the ultrasonic wavelength. Attenuation of the ultrasonic signal is the loss of ultrasonic signal as the signal travels. Reflection of the ultrasonic signal is the bouncing off of the ultrasonic signal from an object (e.g., a vessel wall) that is similar in size or larger than the ultrasonic wavelength. Transmission of the ultrasonic signal is the passing of the ultrasonic signal through a medium. As it travels, the ultrasonic signal is scattered, attenuated, reflected, and/or transmitted. The portions of the reflected and/or scattered ultrasonic signals that return to the transducers are detected as echoes.
In the ultrasound art, steering refers to changing the direction of an ultrasonic beam. Aperture refers to the size of the transducer or group of transducer elements being used to transmit or receive an ultrasonic signal. The transmit aperture is the size of the transducer or group of transducers used to transmit an ultrasound signal, and receive aperture is the size of the transducer or group of transducers used to receive an ultrasound signal. Apodization refers to applying a weighting profile to the signals across the transducer aperture to produce ultrasound beams with reduced sidelobe spreading. Electronic focusing refers to applying relative time and/or phase shifts to signals across the transmit or receive transducer array elements to account for time-of-flight differences.
A conventional process of producing, receiving, and analyzing an ultrasonic signal (or beam) is called beam forming. The production of ultrasonic signals optionally includes apodization, steering, focusing, and aperture control. In conventional beamforming, RF echo data is acquired across a transducer array and processed to generate a one-dimensional set of echolocation data In a typical implementation, a plurality of ultrasonic beams are used to scan a multi-dimensional volume.
In electronic focusing, the transmit aperture of the transducer is apodized and electronically focused to form a transmit beam, and a large number (typically over 100) of transmit beams are generated and steered (as for a sector scan) along different scan lines to cover the entire scan plane.
To create two-dimensional (2D) B-mode images of tissue and 2D color flow images of moving blood, the echoes are detected and converted into electronic signals by the receive transducer aperture elements. Through parallel electronic channels the signals in different frequency bands are subject to amplification, digitization, frequency downshifting, apodization, focusing, steering and other filtering operations in order to generate echolocation data along the scan direction. Depending on the front-end architecture design, the order in which the above processing are performed may vary. Any processing such as amplification, which occurs before digitization would be implemented using analog electronic circuits.
In most ultrasound receivers, the echo signals are shifted down in frequency by means of frequency mixers and filters, to generate the in-phase (I) and quadrature (Q) signals which are centered at a much reduced RF frequency, but contain the same information bandwidth as the RF signals. For color flow processing, the RF spectrum is shifted down to baseband and the resultant I/Q components are also referred to as baseband components. The advantage of using I/Q echo components is that they can be digitized and processed at much lower sampling rates due to their reduced Nyquist bandwidths.
The I/Q echo data is furnished to the B-mode and color flow image processors for amplitude and motion detection respectively. For B-mode, the echo amplitude can be computed simply by taking the square root of I2+Q2. The detected data from different transmit events are compiled into 2D acoustic data sets, which are then converted by the scan-converter into X-Y format of, for example, 480xc3x97640 pixels (picture elements), for video display.
In B-mode imaging, the brightness of a pixel is based on the detected echo amplitude, whereas in color flow imaging, the color of a pixel is based on mean velocity and/or power of detected echoes from moving parts of the medium under investigation. In color flow imaging, the color flow image is formed within a region of interest (ROI), which is over-written onto the B-mode image by a video image processor such that, for example, the composite image depicts blood flow within the ROI according to a color scale, while surrounding stationary tissues are displayed in a gray-scale.
In color flow imaging, for each scan line within the user-specified ROI, a set of transmit beams are fired repeatedly at some pulse repetition frequency (PRF), in order to detect moving blood. Fundamentally, any motion of the medium under investigation relative to the ultrasound transducer produces the well-known Doppler effect in which the frequency of the reflected echo is shifted from that of the transmit frequency fo by an amount fd that is proportional to the target speed in the direction of the ultrasonic beam. That is, the frequency of the reflected signal is fo+fd. A medium under investigation that is moving towards the transducer will compress the incident ultrasonic wave thereby producing a positive Doppler frequency shift in the reflected echo. Conversely, a target that is moving away from the transducer will produce a negative Doppler frequency.
Mathematically, the Doppler frequency shift fd can be derived as follows. Suppose the target (e.g. red blood cells) is moving at velocity v, which makes an angle xcfx86 with respect to the sound beam. This means that the target velocity component in the direction of the sound waves is u=v cos(xcfx86). Over a short time interval xcex94t, the change in round-trip distance between the target and the ultrasonic source (transducer) is xcex94d=2uxcex94t. Assuming u less than  less than c (speed of sound), xcex94d translates into to a phase shift xcex94xcex8=2xcfx80xcex94d /xcex, where xcex=c/fo is the ultrasound wavelength. Hence, the Doppler frequency shift induced by target motion is
fd=xcex94xcex8/(2xcfx80xcex94t)=2fo(u/c)=2fo(v/c)cos(xcfx86).
In practice, Doppler frequencies due to blood flow in humans and animals are in the kHz range, which are much smaller than the transmit radio frequencies. A minimum of two transmit signals must be fired along each scan line in order to generate a measurable phase change between the returning echoes from two successive firings. At a Pulse Repetition Interval (PRI)=1/Pulse Repetition Frequency (PRF), the phase change between echoes from two successive firings is xcex94xcex8=2xcfx80fd/PRF. Color image processors create a color velocity image by estimating xcex94xcex8 for each acoustic point, and then converting it into either Doppler frequency or velocity unit, which is then mapped to a 2D pixel display image according to a color versus velocity or Doppler frequency scale.
Since the instantaneous phase of the returning RF echo from each transmission of an ultrasonic beam is equivalent to the angle of its baseband I and Q components; i.e., xcex8=tanxe2x88x921(Q/I), the phase change over two successive transmit and receive cycles is simply given by
xcex94xcex8=tanxe2x88x921(Q1/I1)xe2x88x92tanxe2x88x921(Q2/I2).
In practice, there are two significant challenges in producing a real-time color flow image. First, the echoes returning from moving blood are generally very weak, so typically a packet of several or more transmit and receive cycles are needed to detect flow along a particular scan line. Color Doppler velocity estimation then involves computing the mean phase change  less than xcex94xcex8 greater than  per PRI from the received echo signals. As will be defined in a later section, a common method of estimating  less than xcex94xcex8 greater than  is to evaluate the first-ordered autocorrelation function of {In,Qn} over the transmit packet (n=1, 2, 3 . . . ) for each acoustic point in the medium under investigation. In other words the autocorrelation is between all possible pairs of {In,Qn} and {Im,Qm} at the same position, where n and m represent different time indices within a color Doppler data packet.
The second practical challenge stems from the fact that the tissue medium, and especially bones and tissue layers that comprise the vessel walls of the insonified blood vessel, often produce very strong reflections that are orders of magnitude larger than the backscattered signals from blood. Without first removing the clutter, the I/Q data analysis would be dominated by clutter effects and will not reflect the desired flow signal properties. To compound this problem, the reflecting structures in the medium under investigation (such as the body) are often moving at low velocities (due to breathing and/or cardiac motion, for example), which means the corresponding clutter signals may also contain Doppler frequency components or phase changes that can register as xe2x80x9ccolor flashesxe2x80x9d in the color flow image.
In order to provide a common framework for understanding the clutter problem, and the new and existing solutions, it is helpful to visualize (FIG. 1) the flow signal component and any unwanted DC or low-frequency clutter component in the Doppler frequency domain (even though color flow image processors don""t actually need to compute the Doppler frequency spectrum from the input I/Q data.)
FIG. 1 is a plot 100 of spectra of clutter and flow components, and a High Pass Filter (HPF) frequency response. FIG. 1 shows frequency spectra of color flow I/Q data having a clutter spectrum 102, and a flow signal spectrum 104, and a HPF frequency response (HPF1) 106 plotted on an x-axis 108 and y-axis 110. X-axis 108 is in units of Hertz (Hz), and y-axis 110 is in units of decibels (dB). The signal spectra 102 and 104 are normalized in magnitude such that the clutter spectrum peak is 0 dB. For the HPF response HPF1106, 0 dB means the filter gain is unity at that particular frequency. In general, clutter spectrum 102 could be the frequency spectrum of a stationary object (such as a bone) and/or of the relatively slow motion of other objects within the medium under investigation. For example, clutter spectrum 102 in FIG. 1 represents the reflected signal that contains a very low Doppler frequency spectrum ranging from 0 to about 30 Hz, with the peak at about 15 Hz. Flow spectrum 104 represents the frequency spectrum of a typical flow from a fluid such as blood within a medium under investigation.
In the body, tissue motion may be caused by breathing, cardiac motion, or simply transducer motion due to the operator, which are generally of a lower speed than blood flow in detectable vessels. The average power of the clutter may be up to 40 dB stronger than the flow signal in the time domain, so that the peak of clutter spectrum 102 maybe 50 dB higher than that of flow spectrum 104.
Three approaches to clutter removal in conventional color flow imaging include using a High Pass Filter (HPF), using a Fast Fourier Transform (FFT), and using a clutter model. These are summarized as follows:
When using a HPF, having for example frequency response HPF1106, the ultrasound signal from the transducer is passed through a linear high pass Finite Impulse Response (FIR) or Infinite Impulse Response (IIR) filter in the time domain. A high pass filter response such as HPF1106 shows which frequency band is attenuated, and which frequency band is allowed to pass. The amplitude of the signal component at a given frequency is reduced by the HPF1106 response (e.g. xe2x88x9250 dB) at that frequency. For example at 460 Hz, the signal amplitude is reduced by xe2x88x928 dB or about 40%. In the frequency domain, clutter spectrum 102 tends to concentrate around the lowest frequency bins. HPF1106 is effective for rejecting the relatively low frequency clutter spectrum 102.
In FIG. 1, HPF1106 corresponds to the frequency response of a filter whose xe2x88x9250 dB stopband edge is at a normalized frequency (i.e., a frequency divided by PRF/2) of 10%.
When using an FFT, the FFT is taken of basebanded I/Q data samples and then the power in the lowest frequency bins is set to zero. This can be viewed as frequency-domain filtering, or a form of clutter modeling in which the data is projected onto a series of complex exponentials of various Doppler shift frequencies.
When using a clutter model (see U.S. Pat. No. 5,228,009, incorporated herein by reference) the part of data samples that represent the clutter (e.g., the time domain representation of clutter spectrum 102) is projected onto a set of low order orthonormal basis functions (e.g., Legendre polynomials), thereby forming a model or fitted curve of the clutter. Then the sum of projections (i.e., the fitted curve) is subtracted from the data samples thereby subtracting the modeled clutter from the data. Using the low frequency components of a frequency spectrum (which may have been obtained via an FFT) to represent the clutter is a special case of clutter modeling.
In all the above three approaches, appropriate parameters are selected that depend on the tissue velocity, such that only the clutter and not the blood flow signal is subtracted from the data or suppressed. Choosing the appropriate parameters is equivalent to choosing the cutoff frequency (and the order) of the high pass filter or to selecting the highest order for the basis functions in the clutter modeling approach. If the filter cutoff is always set high to reject the highest possible clutter frequencies, then some low flow signals may not be detected well. If the filter cutoff frequency is always set low, color flashes may result in the image whenever significant clutter power due to tissue motion is present above the cutoff frequency. Hence, various adaptive clutter suppression techniques have been proposed in the prior art as follows.
U.S. Pat. No. 6,309,357 uses two or more clutter filters of different frequency responses to process each color data packet in parallel and then select or combine the best results for velocity estimation. U.S. Pat. Nos. 5,349524 and 5,445,156 estimate clutter velocity and bandwidth using the standard autocorrelation method, and then either shift the clutter frequency down to DC before high pass filtering, or apply a complex filter with a notch at the estimated clutter frequency. U.S. Pat. No. 5,349,525 estimates the clutter velocity and bandwidth and excise the corresponding bins in the FFT spectrum of the data. U.S. Pat. No. 5,228,009 starts with the lowest order of basis functions (mean removal), computes and subtracts projections from the data onto the basis functions, and then checks the residual energy. The process is repeated for the next lowest order until residual energy falls below a predefined threshold.
In U.S. Pat. Nos. 5,349524, 5,349;525, and 5,445,156, the velocity estimation is performed twice for each packet or acoustic point in the color flow image. Specifically, the velocity estimation is performed the first time to estimate mean clutter velocity, and the second time to estimate the mean flow velocity after the clutter has been subtracted out. Also, the standard autocorrelation method involves a division and an arctangent operation, which are computationally expensive. In U.S. Pat. No. 5,228,009, as the order of filtering increases, the amount of computations required for computing the projection onto the basis function based on the least squares criterion also increases and is quite computationally intensive.
In U.S. Pat. No. 5,782,769, a high pass filter suitable for clutter from non-moving sources is applied prior to velocity estimation, and a separate nonlinear xe2x80x9cmin-maxxe2x80x9d filter is used across image frames to suppress color flashes that may result from tissue motion. While it is true that in general, color flashes can be rejected by such post processing or other simpler threshold techniques based on the total power and mean velocity estimates, if a weaker flow signal is also present, it will likely get thrown out with the flash artifact.
An adaptive clutter removal method is provided that can suppress color flash artifacts without compromising low flow detection. The method utilizes an adaptive high pass filter which is applied to the in-phase (I) and quadrature (Q) components of a given flow data packet prior to flow parameter estimation. The clutter may be detected and the cutoff frequency may be adjusted iteratively, may be adjusted on a point-by-point or region-by-region basis, and may be adjusted dynamically or in real time, while collecting the data for other acoustic points. For example, in an embodiment while data for one frame is imaged, a second frame is filtered, and a third frame is collected.
In an embodiment two criteria are used to detect the clutter, which are the magnitude of the total signal power being higher than a given threshold and the mean Doppler frequency (proportional to mean velocity) being lower than a given clutter frequency threshold. Ordinarily, calculating the mean frequency or phase change entails taking an arc tangent of the real and imaginary parts of the first order autocorrelation function of the I/Q data. However, determining whether the mean frequency change is less than a threshold does not require actually calculating the mean frequency change. Instead, in an embodiment, a check is performed to see if the real part times a multiplicative factor is greater than the absolute value of the imaginary part of the first order autocorrelation function. The multiplicative factor is determined by the ratio of a clutter frequency threshold to the pulse repetition frequency.
Although the method is equally applicable to I/Q flow data derived from 2D or 3D volume scanning based on conventional line-by-line beam forming, the preferred embodiment, for which this is a continuation-in-part, is referred to as area forming which is enabled by broadbeam technologies. Broad beam technologies refer to systems and methods that include or take advantage of techniques for generating a broad ultrasound beam from a single ultrasonic pulse, and analyzing the returning echoes to yield multidimensional spatial information.
The receive system architecture for the preferred broad beam system utilizes software running on a set of Digital Signal Processing (DSP) chips to perform all of the image processing operations after area forming. Although the adaptive clutter filter can also be implemented in hardware, the programmability and scalability of DSP chips provide an ideal match to the algorithmic nature of the adaptive clutter filter. For example, to achieve more optimal clutter filter performance, the number of iterations and high pass filter choices can be increased in concert with technological advances in the DSP chip family.
Area forming is the process of producing, receiving, and analyzing RF echoes from a medium under investigation, that optionally includes apodization, steering, focusing, and aperture control, where a two-dimensional set of echolocation data can be generated using only one ultrasonic beam. Nonetheless, more than one ultrasonic beam may still be used with the area forming even though only one is necessary. Area forming is a process separate and distinct from beam forming. Area forming may yield an area of information using one transmit and/or receive cycle, in contrast to beam forming, which typically only processes a line of information per transmit and/or receive cycle.
Volume forming is the process of producing, receiving, and analyzing an ultrasonic beam, that optionally includes apodization, steering, focusing, and aperture control, where a three dimensional set of echolocation data can be generated using only one ultrasonic beam. Nonetheless, multiple ultrasonic beams may be used although not necessary. Volume forming is a superset of area forming.
Multidimensional forming is the process of producing, receiving, and analyzing an ultrasonic beam that optionally includes apodization, steering, focusing, and aperture control. Using multidimentional forming a two or more dimensional set of spatial echolocation data can be generated with only one ultrasonic beam. Nonetheless, multiple ultrasonic beams may be used although not necessary. Multidimensional forming optionally includes non-spatial dimensions such as time and velocity.